Laser-based Tracking of People and Vehicles by Multiple Mobile

Robots

Masafumi Hashimoto

, Ryunosuke Izumi

, Yuto Tamura

and Kazuhiko Takahashi

Faculty of Science and Engineering, Doshisha University, Kyotanabe, Kyoto, Japan

Graduate School of Doshisha University, Kyotanabe, Kyoto, Japan

Keywords: Moving-object Tracking, Laser Scanner, Mobile Robot.

Abstract: This paper presents laser-based tracking of moving objects conducted by a group of mobile robots located

near one another. Each robot finds moving objects such as people, cars, and bicycles in its own laser-

scanned images using a binarized occupancy-grid-based method. It then sends laser measurements related to

the detected moving objects to a central server. The central server estimates the pose and size of the moving

objects via the Kalman filter based on received measurements; it then feeds that information back to the

robots. Rule-based and global-nearest-neighbor-based data associations are applied for matching of tracked

objects and laser measurements in multitarget environments. In this cooperative tracking method, the central

server collects the laser measurements from all robots; hence, the robots can always track invisible or

partially invisible objects. The experimental results for two robots in an outdoor environment validate our

tracking method.

1 INTRODUCTION

Tracking (i.e., estimating the motion of) multiple

moving objects is an important issue for the safe

navigation of mobile robots and vehicles. The use of

stereo cameras or laser scanners (LS) in mobile

robotics and vehicle automation has attracted

considerable interest (Arra and Mozos, 2010, Mertz

et al., 2013, Ogawa et al., 2011, Sun et al., 2006).

We have presented a people-tracking method that

uses LS mounted on mobile robots and automobiles

(Hashimoto et al., 2006, Sato et al., 2010). To

introduce robots (such as service and rehabilitation

robots) into human environments, higher accuracy

and reliability of moving-object tracking systems are

required.

Most conventional moving-object tracking

focuses on people under the assumption that a

moving object is a mass point. However, in the real

world, many kinds of moving objects, such as

people, cars, bicycles, and motorcycles, exist.

Therefore, we should treat a moving object as a rigid

body and estimate both pose (position and velocity)

and the object size. Tracking of a rigid body is

known as extended object tracking, and many

studies related to extended object tracking have been

conducted (Fayad and Cherfaoui, 2007, Miyata et

al., 2009, Zhao et al., 2012).

Recently, many studies related to multirobot

coordination and cooperation have also been

conducted. When multiple robots are located near

one another, they can share their sensing data

through intercommunication. Thus, the multirobot

team can be considered a multisensor system.

Therefore, even if moving objects are located

outside the sensing area of a robot, the robot can

recognize them based on tracking data from the

other robots in the team. Hence, multiple robots can

improve the accuracy and reliability of tracking

moving objects (Chou, 2011, Tsokas and

Kyriakopoulos, 2012).

As shown in Fig. 1, in an intelligent transport

system (ITS), if tracking data are shared with

neighboring vehicles, each vehicle can efficiently

recognize moving objects. Therefore, an advanced

driver assist system can be built that detects people

suddenly running on roads and vehicles making

unsafe lane changes in crowded urban environments.

For this purpose, our previous work (Kakinuma

et al., 2012, Ozaki et al., 2012) presented a people-

tracking method using multiple mobile robots. In

this paper, we extend our previous method (people

tracking) to tracking both people and vehicles; their

pose and size are estimated using multiple mobile

522

Hashimoto M., Izumi R., Tamura Y. and Takahashi K..

Laser-based Tracking of People and Vehicles by Multiple Mobile Robots.

DOI: 10.5220/0005084205220527

In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO-2014), pages 522-527

ISBN: 978-989-758-040-6

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: Example of cooperative tracking in ITS.

Figure 2: Overview of the mobile robot system.

robots.

For simplicity, in this paper, moving-object

tracking by multiple mobile robots is referred to as

cooperative tracking, whereas that by an individual

robot in a team is referred to as individual tracking.

The rest of the paper is organized as follows. Section

2 gives an overview of our experimental system. In

Section 3, cooperative tracking is presented. In

Section 4, to validate our method, we describe an

experiment of moving-object tracking by using two

mobile robots in an outdoor environment; we then

present our conclusions.

2 EXPERIMENTAL SYSTEM

Figure 2 shows the mobile robot system used in our

experiments. Each of the two robots has two

independently driven wheels. A wheel encoder is

attached to each drive wheel to measure the wheel’s

velocity. A yaw rate gyro is attached to each robot’s

chassis to sense the turn velocity. These internal

sensors calculate the robot’s pose based on dead

reckoning.

Each robot is equipped with a forward-looking

LS (SICK LMS100). It captures laser-scanned

images that are represented by a sequence of

distance samples in a horizontal plane of 270 deg.

Each robot is also equipped with RTK–GPS

(NovAtel GPS-702-CG). The sampling period of the

sensors is 10 Hz. The angular resolution of the LS is

0.5 deg, and each scan image consists of 541

distance samples. We use broadcast communication

by wireless LAN to exchange information between

the central server and the robots.

3 MOVING-OBJECT TRACKING

3.1 Overview

As shown in Fig. 3, each robot independently finds

moving objects in its own laser image based on a

binarized occupancy-grid method (Hashimoto et al.,

2006). The robot uploads laser measurements related

to moving objects to a central server.

Laser measurements (positions) from the same

moving object have similar values, whereas those

from different objects are significantly different.

Thus, the central server clusters laser measurements

by checking the gap between two adjacent

measurements. Subsequently, the server tracks

moving objects (estimates their size, position, and

velocity) and transmits the tracking data to the

robots.

The grid map is represented on a world

coordinate frame. To map the laser-scanned images

onto the coordinate frame, each robot needs to

identify its own pose with a high degree of accuracy

on the world coordinate frame. To define the world

coordinate frame, we consider the GPS base station

as the origin. Each robot determines its own pose

based on dead reckoning and GPS information via

the extended Kalman filter.

Figure 3: System overview of cooperative tracking.

Laser-basedTrackingofPeopleandVehiclesbyMultipleMobileRobots

523

3.2 Size and Pose Estimation

We assume that the shape of the moving object is

represented by a rectangle with width W and length

L. As shown in Fig. 4, we define an x

-coordinate

frame on which the y

-axis aligns with the heading

direction of a tracked object. From clustered laser

measurements related to a moving object (hereafter,

moving-object measurements), we extract the width

meas

and length L

meas

. The size of the tracked object

is then estimated by the following equation (Fayad

and Cherfaoui, 2007):











)(

kmeaskk

LLGLL

WWGWW

(1)

where W and L are estimates of width and length,

respectively, k and k

1 are time steps. G is the filter

gain, given by

pG )1(1 

, and p is a

parameter; the larger the value of p, the more

reliable the current measurements, W

meas

and L

meas

To extract W

meas

and L

meas

from the moving-object

measurements, we need to obtain the heading

direction of the tracked object; as shown in Fig. 4,

we extract two lines based on the split and merge

method (Nguyen et al., 2009) from the moving-

object measurements and determine the heading

direction of the tracked object from the orientation

of the lines. When we cannot extract the two lines,

we determine the heading direction of the tracked

object from the velocity estimate of the object,

which is estimated by the following method.

We define the centroid position of the rectangle

estimated by Eq. (1). From the centroid position, the

pose of the tracked object (position and velocity,

Figure 4: Size estimate. Red circles indicate moving-

object measurements. Green lines indicate extracted lines

based on these measurements. Orange rectangle indicates

the estimate rectangle, and orange circle indicates the

centroid of the rectangle.

estimated by the Kalman filter under the assumption

that the object moves at a nearly constant velocity.

Objects appear in and disappear from the sensing

area of the LS. They also interact with and are

occluded by each other and other objects in the

environment. To maintain the reliable tracking under

such conditions, we implement a rule-based

tracking-management system (Hashimoto et al.,

2006).

3.3 Data Association

To track objects in multi-object and multi-

measurement environments, we need data

association (one-to-one matching of tracked objects

and laser measurements); a validation region is set

around the predicted position of each tracked object.

The shape of the validation region is rectangular,

and its length and width are 0.8 m longer than those

for the object estimated at the previous time step.

We refer to the representative of grouped

moving-object measurements as the representative

measurement. Representative measurements inside

the validation region are considered to originate

from the tracked object and are used to update the

position of the tracked object using the Kalman filter,

whereas those outside the validation region are

identified as false alarms and discarded.

As shown in Figs. 5 and 6, in the real world,

multiple representative measurements often exist

inside a validation region; multiple tracked objects

also compete for representative measurements. To

achieve a reliable data association (matching of

tracked objects and representative measurements),

we introduce the following rules:

a) Person: Because person sizes are small, a

person usually results in one representative

measurement. Thus, if a tracked object is considered

a person, matching of a tracked person and a

representative measurement (one-to-one matching)

is performed.

b) Vehicle: Because vehicle sizes are large, as

shown in Fig. 5, a vehicle often results in multiple

representative measurements. Thus, if a tracked

object is considered a vehicle, matching of a tracked

vehicle and representative measurements (one-to-

many matching) is performed.

Based on the estimated size of the tracked object,

we decide whether the object is a person or a

vehicle; if the estimated size in length or width is

larger than 0.8 m, the object is determined to be a

vehicle; otherwise, a person.

On urban streets, people often move near

vehicles, whereas vehicles move far away from each

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Figure 5: Laser measurements obtained using two robots.

Figure 6: Data association. Black and red circles indicate

tracked objects and moving-object measurements,

respectively. Light blue triangles indicate representative

measurements for moving-object measurements. VG stands

for validation region.

other. Thus, when representative measurements of

people exist in the validation region of a tracked

vehicle, they might be matched to the tracked

vehicle. To avoid that situation, we begin data

association with people.

As shown in Fig. 6, if a tracked object T2 is

determined to be a person, the representative

measurement M3 is matched with T2 based on the

global nearest neighbor (GNN) method

(Konstantinova et al, 2003). Next, if a tracked object

T1 is determined to be a vehicle, the two

representative measurements M1 and M2 are

matched with T1. The representative measurement

M4 that is not matched with any tracked objects is

considered either to originate from a new object or

to be a false alarm. Therefore, we tentatively initiate

tracking of the measurement with the Kalman filter.

If the measurement is always visible, it is considered

to originate from a new object and tracking is

continued. If the measurement soon disappears, it is

considered to be a false alarm and tentative tracking

is terminated.

4 EXPERIMENTAL RESULTS

We evaluated our tracking method by conducting an

experiment in an outdoor environment, shown in

Fig. 7(a). Two robots that are moving around track

two people, a car, and a motorcycle; Fig. 8 shows

their movement paths. The moving speed of the

robots, people, car, and motorcycle were about 3

km/h, 5 km/h, 15 km/h, and 20 km/h, respectively.

Experimental time was 27 s (270 scans).

(a) Photo of the experimental environment.

(b) Tracking result.

Figure 7: Moving-object tracking experiment. In (b), black

rectangles indicate the estimated size of moving objects.

Green and blue rectangles indicate the validation regions

of cars and people, respectively. Blue bars indicate the

estimated the moving direction. Red and blue points

indicate laser images taken by robots #1 and #2,

respectively.

Table 1: Tracking duration.

Cooperative

tracking

Individual tracking by

Robot #1 Robot #2

Moving

object

#1 64–177[scan] 64–177 None

#2 65–270 65–270 188–243

#3 89

–

183 89

–

183 96

–

136

#4 127–182 127–181 169–182

Laser-basedTrackingofPeopleandVehiclesbyMultipleMobileRobots

525

(a) Estimated track (pose) of moving objects. (b) Estimated size of vehicle #3.

Figure 9: Pose and size estimated by the cooperative tracking of two robots. In (b), red and black lines indicate the

estimated length and width, respectively, of vehicle #3; two dashed lines indicate the true length and width of vehicle #3.

(a) Estimated track (pose) of moving objects. (b) Estimated size of vehicle #3.

Figure 10: Pose and size estimated by the individual tracking of robot #1.

(a) Estimated track (pose) of moving objects. (b) Estimated size of vehicle #3.

Figure 11: Pose and size estimated by the individual tracking of robot #2.

Figure 8: Movement path of moving objects.

In this experiment, the filter gain G from Eq. (1) is

determined as follows:

















scan 10for369.001.01

scan 10for01.01

Figure 7(b) shows the tracking results at 16 s

(160 scans). Figure 9 shows the tracking of people

and vehicles as well as the size of vehicle #3, as

estimated by two robots (cooperative tracking). For

comparison, individual tracking by each robot was

also conducted. The tracking results for robots #1

ICINCO2014-11thInternationalConferenceonInformaticsinControl,AutomationandRobotics

526

and #2 are shown in Figs. 10 and 11, respectively.

Table 1 shows the tracking duration.

These results show that cooperative tracking

using two robots can provide better tracking

accuracy than individual tracking using either robot

#1 or #2.

5 CONCLUSIONS

This paper presented a laser-based method for

tracking of moving objects (people and vehicles)

that uses multiple mobile robots located near one

another. The size and pose (position and velocity) of

the objects were estimated, and the method was

validated by an experiment of people and vehicle

tracking using two robots.

In our method, robots find moving objects in

their sensing area and transmit object information to

a central server, which then estimates the size and

pose for each moving object. Such a server-client

system is weak from the view-point of system

dependability and computational burden. Future

research will be directed to the design of a

decentralized architecture in moving-object tracking.

ACKNOWLEDGEMENTS

This study was partially supported by Scientific

Grants #23560305 and #26420213, Japan Society

for the Promotion of Science (JSPS).

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